基于Gammatone滤波器组时频谱和 卷积神经网络的海底底质分类*

为了有效利用海底底质信号完成海底底质的分类识别，本文提出一种将深度学习方法和底质信号相结合实现底质分类识别的方法。首先利用Gammatone滤波器组计算底质侧扫图像信号的时频谱，最后利用卷积神经网络(Convolutional Neural Networks, CNN)对得到的时频谱进行分类识别完成底质分类。实验结果表明该方法的底质分类准确率平均达到97.64%，相对于其他方法，分类性能更加优越；同时利用该方法分类海试数据，结果证明该方法具有一定的泛化能力。本文研究结果对实际的海底底质分类具有一定参考意义。

Seabed sediment classification based on gammatone filter banks time-frequency spectrum and convolutional neural networks

In order to effectively use sea bottom sediment signal to accomplish the classification and recognition of the sediments, our paper proposes a method of combining the deep learning and the sediment signal to achieve the classification and identification of the sea bottom sediment. First, the Gammatone filter banks is used to calculate the time-frequency spectrum of sediments side scan sonar image signals. In the end, using a CNN model to classify the time-frequency spectrum calculated by Gammatone filter banks. The experimental results show that the classification and recognition accuracy of sediments by this method can averagely reach 97.64%, which is superior to other methods in classification performance, and the results of using this method to classify the sea trial data show the means proposed by this paper has a certain generalization ability. The results of this study have specific reference significance for actual seabed sediments classification.